Import data and define variables:

"~/Projects/ETH/Alessia/mobilome/ps_combined_tax_clean.rds" %>% 
  readRDS() -> ps_all

ps_all %>% 
  subset_samples(Model == "Human") %>% 
  filter_taxa(function(x) sum(x > 0) > 0, TRUE) -> physeq

out_pptx = "~/Projects/ETH/Alessia/mobilome/human/graph.pptx"

physeq %>% 
  tax_table() %>% 
  data.frame() -> tax_mapping

physeq_tmp <- physeq
tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Resistance_Mechanism_multi", "Best_Hit_ARO")]
# tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Best_Hit_ARO", "Drug_Class")]

physeq_tmp %>% 
  tax_glom(., taxrank = "Best_Hit_ARO") %>% 
  tax_glom(., taxrank = "Resistance_Mechanism_multi") -> physeq_res_multi

taxa_names(physeq_res_multi) <- tax_table(physeq_res_multi)[,"Resistance_Mechanism_multi"]

physeq_res_multi

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 6 taxa and 49 samples ]:
## sample_data() Sample Data:        [ 49 samples by 60 sample variables ]:
## tax_table()   Taxonomy Table:     [ 6 taxa by 2 taxonomic ranks ]:
## taxa are rows

physeq_tmp <- physeq
tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Drug_Class_multi", "Best_Hit_ARO")]
# tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Best_Hit_ARO", "Drug_Class")]

physeq_tmp %>% 
  tax_glom(., taxrank = "Best_Hit_ARO") %>% 
  tax_glom(., taxrank = "Drug_Class_multi") -> physeq_drug_multi

taxa_names(physeq_drug_multi) <- tax_table(physeq_drug_multi)[,"Drug_Class_multi"]

physeq_drug_multi

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 19 taxa and 49 samples ]:
## sample_data() Sample Data:        [ 49 samples by 60 sample variables ]:
## tax_table()   Taxonomy Table:     [ 19 taxa by 2 taxonomic ranks ]:
## taxa are rows

physeq_tmp <- ps_all
tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Drug_Class_multi", "Best_Hit_ARO")]
# tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Best_Hit_ARO", "Drug_Class")]

physeq_tmp %>% 
  tax_glom(., taxrank = "Best_Hit_ARO") %>% 
  tax_glom(., taxrank = "Drug_Class_multi") -> ps_all_drug_multi

taxa_names(ps_all_drug_multi) <- tax_table(ps_all_drug_multi)[,"Drug_Class_multi"]

ps_all_drug_multi

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 19 taxa and 92 samples ]:
## sample_data() Sample Data:        [ 92 samples by 60 sample variables ]:
## tax_table()   Taxonomy Table:     [ 19 taxa by 2 taxonomic ranks ]:
## taxa are rows

physeq_tmp <- physeq
tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Drug_Class", "Best_Hit_ARO")]
# tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Best_Hit_ARO", "Drug_Class")]

physeq_tmp %>% 
  tax_glom(., taxrank = "Best_Hit_ARO") %>% 
  tax_glom(., taxrank = "Drug_Class") -> physeq_drug

taxa_names(physeq_drug) <- tax_table(physeq_drug)[,"Drug_Class"]

physeq_drug

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 38 taxa and 49 samples ]:
## sample_data() Sample Data:        [ 49 samples by 60 sample variables ]:
## tax_table()   Taxonomy Table:     [ 38 taxa by 2 taxonomic ranks ]:
## taxa are rows

physeq_tmp <- physeq

tax_table(physeq_tmp) <- tax_table(physeq_tmp)[,c("Best_Hit_ARO", "Drug_Class")]

physeq_tmp %>% 
  tax_glom(., taxrank = "Best_Hit_ARO") -> physeq_AMRgn

taxa_names(physeq_AMRgn) <- tax_table(physeq_AMRgn)[,"Best_Hit_ARO"]

physeq_AMRgn

## phyloseq-class experiment-level object
## otu_table()   OTU Table:          [ 104 taxa and 49 samples ]:
## sample_data() Sample Data:        [ 49 samples by 60 sample variables ]:
## tax_table()   Taxonomy Table:     [ 104 taxa by 2 taxonomic ranks ]:
## taxa are rows

Define colors for Drug classes:

set.seed(123)
col <- distinctColorPalette(ntaxa(ps_all_drug_multi))

names(col) <- taxa_names(ps_all_drug_multi)

Define colors for Treatment - no matter concentration / fermentation / reactors:

# myCol <- viridis::viridis(n = get_variable(physeq_drug_multi, "Treatment") %>%  
#                             levels() %>% 
#                             length() + 20)
# myCol
# 
# scales::show_col(myCol,
#                  cex_label = 0.5)

treat_col = get_variable(physeq_drug_multi, "Treatment") %>%  
  levels()


# treat_col <- c("#1B1919", "#7C8181", "#00468B", "#2589AE","#029AAF", "#463161", "#7C66A2", "#ad99cf")

treat_col <- c("#1B1919", "#7C8181", "#00468B", "#3085AD", "#42B540", "#9E172E", "#B43C15", "#AD7987")


names(treat_col) <- get_variable(physeq_drug_multi, "Treatment") %>%  
  levels()

scales::show_col(treat_col,
                 cex_label = 0.5)

physeq_AMRgn %>%
  filter_taxa(function(x) sum(x > 0) > 0, TRUE) %>% 
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  left_join(tax_mapping,
            by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
            suffix = c("_x", "")) %>% 
  mutate(Best_Hit_ARO = fct_reorder(Best_Hit_ARO, Abundance, .desc=FALSE)) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # df$Item = factor(df$Item, levels = a_lot$Item[order(a_lot$percent)])
  
  # tmp$variable <- factor(melted$variable, levels=rev(levels(melted$variable)))
  # mutate(Best_Hit_ARO =  fct_reorder(Best_Hit_ARO, logTPM)) %>% 
  # mutate(Best_Hit_ARO =  fct_relevel(Best_Hit_ARO, logTPM)) %>% 
  # separate_rows(., Drug_Class,sep = '; ',convert = TRUE) %>% 
  ggplot(mapping = aes(x = as.factor(Day_of_Treatment),
                       y = Best_Hit_ARO,
                       fill = logTPM,
  )) +
  facet_grid(Drug_Class_multi  ~ Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free") + #,  labeller = labeller(Drug_Class_multi = label_wrap_gen(width = 15)))  +
  # facet_grid(. ~ Reactor_Treatment, scales = "free", space = "free", drop = TRUE,labeller = labeller(Drug.Class = label_wrap_gen(width = 15))) +
  #we adjust the vertical labels such that it is printed correctly. 
  geom_tile() +
  theme_light()  +
  # scale_fill_viridis_c(name = "TPM",
  #                      na.value = "transparent", trans = scales::pseudo_log_trans(sigma = 0.1),
  #                      # trans =  scales::pseudo_log_trans(sigma = 1),
  #                      breaks = c(1, 10, 50, 100, 400), labels = c(1, 10, 50, 100, 400),
  #                      limits = c(0, 500)) + #need to make sure that we include max value of tpm as upper limit
  scale_fill_viridis_c(na.value = "transparent") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        strip.text.x = element_text(size=8),
        #change the size of the label and the angle so the text is printed horizontally
        strip.text.y = element_text(size = 7,angle=0)) -> perma_bbuble

perma_bbuble +
  theme(legend.position = "bottom") +
  scale_y_discrete(label = function(x) stringr::str_trunc(x, 34, side = "center"))  -> perma_bbuble

perma_bbuble

perma_bbuble %>%
  export::graph2ppt(append = TRUE, width = 8, height = 16,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

# perma_bbuble + facet_null() +  facet_grid(.  ~  Model + Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free") +
#  ggside::geom_ysidetile(aes(x = 0, yfill = col[perma_bbuble$data$Drug_Class_multi]), show.legend = TRUE) -> test_annot
# 
# 
# test_annot

pheatmap:

physeq_AMRgn %>%
  filter_taxa(function(x) sum(x > 0) > 0, TRUE) %>% 
  speedyseq::tax_glom("Best_Hit_ARO") %>% 
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #           by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #           suffix = c("_x", "")) %>% 
  # mutate(Sample = fct_reorder(Sample, Model,Treatment,Fermentation,Antibiotic_mg.mL, Day_of_Treatment )) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1)) -> pheatmap_df


pheatmap_df %>% 
  # replace(is.na(.), 0)  %>% 
  # select(Sample, Best_Hit_ARO, logTPM) %>% 
  group_by(Sample, Best_Hit_ARO) %>%
  pivot_wider(names_from =  Sample, Best_Hit_ARO,
              values_from = logTPM) %>% 
  column_to_rownames("Best_Hit_ARO") -> pheatmap_samples

pheatmap_samples %>% 
  replace(is.na(.), 0)  %>% 
  t() %>% 
  vegdist(na.rm = TRUE) %>% 
  hclust() -> clust_t

# pheatmap_samples %>% 
#     replace(is.na(.), 0)  %>%
#     vegdist(na.rm = TRUE) %>% 
#   # cor() %>% 
#   hclust() -> clust

pheatmap_samples %>% 
  pheatmap::pheatmap(cluster_rows = FALSE ,
                     cluster_cols = clust_t,
                     na_col = "transparent",
                     annotation_row = tax_mapping %>% rownames_to_column('tmp_col') %>% 
                       filter(Best_Hit_ARO %in% taxa_names(physeq_AMRgn)) %>% 
                       distinct(Best_Hit_ARO, .keep_all = TRUE) %>% 
                       column_to_rownames("Best_Hit_ARO") %>% 
                       select(Drug_Class_multi, Resistance_Mechanism_multi),
                     annotation_colors = list("Drug_Class_multi" = col,
                                              "Treatment" = treat_col),
                     annotation_col = physeq_AMRgn %>% 
                       sample_data() %>% 
                       data.frame() %>% 
                       select(Model,Treatment,Fermentation,Antibiotic_mg.mL, Day_of_Treatment)) -> p_pheat


p_pheat %>% 
print()

p_pheat %>%
  export::graph2ppt(append = TRUE, width = 14, height = 18,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq_AMRgn %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  left_join(tax_mapping,
            by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
            suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Resistance_Mechanism_multi)) +   
  geom_bar( stat = "identity", colour="black", size=0.025) +
  facet_grid(.  ~   Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("log10 TPM") + 
  theme_light() -> p_mec

p_mec %>% 
  ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2307 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 6,
                    file = out_pptx)

## Warning: Removed 2307 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

# https://newbedev.com/how-to-control-ordering-of-stacked-bar-chart-using-identity-on-ggplot2
physeq_AMRgn %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  left_join(tax_mapping,
            by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
            suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Resistance_Mechanism_multi)) +   
  geom_bar( stat = "identity", position = "fill", colour="black", size=0.00125) +
  facet_grid(.  ~ Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("Proportion") + 
  theme_light() -> p_mec

p_mec %>% 
  ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2120 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 6,
                    file = out_pptx)

## Warning: Removed 2120 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq_drug_multi %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Drug_Class_multi)) +   
  geom_bar( stat = "identity", colour="black", size=0.025) +
  scale_fill_manual(values=col) +
  #geom_bar(stat="identity",colour=NA,size=0) +
  facet_grid( ~  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("log10 TPM") + 
  theme_light() -> p_drug

p_drug

p_drug %>%
  export::graph2ppt(append = TRUE, width = 14, height = 6,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq_drug_multi %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Drug_Class_multi)) +   
  geom_bar( stat = "identity", position="fill", colour="black", size=0.025) +
  scale_fill_manual(values=col) +
  #geom_bar(stat="identity",colour=NA,size=0) +
  facet_grid(. ~ Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x", drop = TRUE) +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("Proportion") +
  theme_light() -> p_drug_prop

p_drug_prop

p_drug_prop %>%
  export::graph2ppt(append = TRUE, width = 11, height = 5,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq_AMRgn_round <- physeq_AMRgn
round(otu_table(physeq_AMRgn_round)) -> otu_table(physeq_AMRgn_round)


physeq_AMRgn_round %>% 
  phyloseq_alphas(phylo = FALSE) -> alpha_df

## Loading required package: metagMisc

## 
## Attaching package: 'metagMisc'

## The following object is masked from 'package:purrr':
## 
##     some

## Loading required package: microbiome

## 
## microbiome R package (microbiome.github.com)
##     
## 
## 
##  Copyright (C) 2011-2020 Leo Lahti, 
##     Sudarshan Shetty et al. <microbiome.github.io>

## 
## Attaching package: 'microbiome'

## The following object is masked from 'package:scales':
## 
##     alpha

## The following object is masked from 'package:vegan':
## 
##     diversity

## The following object is masked from 'package:ggplot2':
## 
##     alpha

## The following object is masked from 'package:base':
## 
##     transform

## Observed richness

## Other forms of richness

## Diversity

## Evenness

## Dominance

## Rarity

## New names:
## * id -> id...1
## * id -> id...62
## * id -> id...85

# plot panels for each treatment
alpha_df %>% 
  pivot_longer(cols = all_of(c("Observed", "diversity_shannon")), values_to = "value", names_to = 'alphadiversiy', values_drop_na  = TRUE) %>%
  mutate(alphadiversiy = fct_relevel(alphadiversiy, c("Observed", "diversity_shannon"))) %>% 
  # filter(!is.na(Fermentation)) %>% 
  # filter(Treatment != "DONOR") %>%
  ggplot(aes(x = Day_of_Treatment, y = value, color = Treatment, shape = as.factor(Fermentation))) +
  geom_point() + 
  geom_line(linetype = "dashed",  size = 0.25,  aes(group = interaction(Treatment,  Fermentation))) +
  labs(title = "AMR Gene Alpha Diversit",
       y = "Alpha Diversity", x = "Days (Treatment)") +
  scale_color_manual(values = treat_col,
                     na.value = "black") +
  # facet_grid(alphadiversiy  ~  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "fixed", drop = TRUE) +
    facet_grid(alphadiversiy  ~  Antibiotic_mg.mL , scales = "free", space = "fixed", drop = TRUE) +
    scale_shape_manual(values = c(15, 19), na.value =  17) + 
  theme_light() -> p_alpha

p_alpha

p_alpha %>%
  export::graph2ppt(append = TRUE, width = 14, height = 5,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

Beta Diversity:

# physeq_AMRgn %>% 
#   sample_data() %>% 
#   data.frame() %>% 
#   mutate(Antibiotic_mg.mL = replace_na(Antibiotic_mg.mL, 0)) -> sample_data(physeq_AMRgn)
  # replace(is.na(.), 0)  %>% 
  # mutate_all(funs(str_replace(., "[^[:alnum:]]", ""))) %>% -> otu_table(physeq) 

physeq_AMRgn %>%
  phyloseq_compute_bdiv(norm = "pc",
                        phylo = FALSE,
                        seed = 123) -> bdiv_list

## Loading required package: ape

## Loading required package: GUniFrac

## Registered S3 method overwritten by 'rmutil':
##   method         from
##   print.response httr

physeq_AMRgn  %>%
  # subset_samples(Treatment != "DONOR") %>%
  phyloseq_plot_bdiv(dlist = bdiv_list,
                     seed = 123,
                     axis1 = 1,
                     axis2 = 2)  -> pcoa

## [1] "bray"
## [1] "sorensen"
## [1] "bjaccard"
## [1] "wjaccard"

# phyloseq_plot_bdiv(bdiv_list,
#                    # m = "CoDa",
#                    seed = 123) -> coda
# 
pcoa$wjaccard$layers = NULL

pcoa$wjaccard + geom_point(size=2,
                           aes(color = Treatment, 
                               fill = Treatment,
                               shape = Fermentation %>%  as.factor(),
                               alpha = Antibiotic_mg.mL)) + 
  geom_path(arrow = arrow(type = "open", angle = 30, length = unit(0.15, "inches")),
            size = 0.05, linetype = "dashed", inherit.aes = TRUE, aes(group=Reactor_Treatment), show.legend = FALSE) +
  scale_alpha_continuous(range=c(0.2, 0.8), breaks = c(0, 20, 90, 200, 600), na.value =  1) +
  scale_fill_manual(values = treat_col,
                    na.value = "black") +
  scale_color_manual(values = treat_col,
                     na.value = "black") +
  scale_shape_manual(values = c(15, 19), na.value =  17) + 
  theme_classic() + guides(fill=FALSE)  -> p1

## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

# p1 + facet_grid(Model ~.)
p1

p1 %>%
  export::graph2ppt(append = TRUE, width = 8, height = 6,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq_AMRgn %>% 
  tax_table() %>% 
  data.frame() %>% 
  rownames_to_column("id") %>% 
  mutate(gene = id) %>% 
  column_to_rownames("id") %>% 
  as.matrix %>% 
  tax_table() -> tax_table(physeq_AMRgn)



physeq_AMRgn  %>%
  # subset_samples(Treatment != "DONOR") %>% 
  phyloseq_add_taxa_vector(dist = bdiv_list$wjaccard,
                           phyloseq = .,
                           figure_ord = p1,
                           tax_rank_plot = "Best_Hit_ARO", taxrank_glom = "Best_Hit_ARO",
                           top_r = 10, fact = 0.4) -> pco_env

# pco_env$plot %>% 
#   export::graph2ppt(append = TRUE, width = 8, height = 6,
#                     file = out_pptx)
pco_env$plot %>% 
    export::graph2ppt(append = TRUE, width = 8, height = 6,
                    file = out_pptx)

## Warning: Removed 3 rows containing missing values (geom_text_repel).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

pco_env$signenvfit %>% 
  DT::datatable()

physeq_drug_multi  %>%
  # subset_samples(Treatment != "DONOR") %>% 
  phyloseq_add_taxa_vector(dist = bdiv_list$wjaccard,
                           phyloseq = .,
                           figure_ord = p1,
                           tax_rank_plot = "Drug_Class_multi", taxrank_glom = "Drug_Class_multi",
                           top_r = 10, fact = 0.4) -> pco_env


# pco_env$plot + facet_grid(Model ~ .) -> pco_env$plot

pco_env$plot

pco_env$plot  %>%
  export::graph2ppt(append = TRUE, width = 8, height = 6,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

pco_env$signenvfit %>% 
  DT::datatable()

Make the heatmap pretty

physeq %>% 
  # speedyseq::tax_glom("Best_Hit_ARO") %>% 
  speedyseq::psmelt() %>% 
  rename(TPM = Abundance) %>% 
  mutate(TPM = na_if(TPM, 0)) %>%
  # mutate(logTPM = log10(TPM + 1)) %>% 
  # filter(Drug_Class %in% c("cephalosporin", "glycopeptide")) %>% 
  # mutate(Drug_Class = fct_relevel(Drug_Class, c("cephalosporin", "glycopeptide"))) %>% 
  filter(Best_Hit_ARO %in% c("vanZA", "vanYA", "vanXA", "vanA", "vanHA", "vanRA", "CTX-M-1")) %>% 
  # mutate(Classification = fct_relevel(Classification, c("MAG", "Chromosome", "Plasmid", "Uncertain - plasmid or chromosomal"))) %>% 
  ggplot(mapping = aes(x = Day_of_Treatment ,
                       y = Best_Hit_ARO ,
                       fill = TPM,
  )) +
  facet_grid(Drug_Class ~  Model + Treatment + Fermentation + Antibiotic_mg.mL, scales = "free",  space = "fixed", drop = TRUE) +
  # facet_grid(. ~ Treatment,  scales = "free", space = "free", drop = TRUE) +
  # facet_grid(. ~ Reactor_Treatment, scales = "free", space = "free", drop = TRUE,labeller = labeller(Drug_Class = label_wrap_gen(width = 15))) +
  #we adjust the vertical labels such that it is printed correctly. 
  geom_tile() +
  theme_bw()  +
  # scale_fill_viridis_c(name = "TPM",
  #                      na.value = "transparent", trans = scales::pseudo_log_trans(sigma = 0.1),
  #                      # trans =  scales::pseudo_log_trans(sigma = 1),
  #                      breaks = c(1, 10, 50, 100, 400), labels = c(1, 10, 50, 100, 400),
  #                      limits = c(0, 500)) + #need to make sure that we include max value of tpm as upper limit
  scale_fill_viridis_c(na.value = "transparent") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        strip.text.x = element_text(size=8),
        #change the size of the label and the angle so the text is printed horizontally
        strip.text.y = element_text(size = 7,angle=0)) -> perma_bbuble

perma_bbuble +
  theme(legend.position = "bottom") -> perma_bbuble

perma_bbuble

perma_bbuble %>%
  export::graph2ppt(append = TRUE, width = 10, height = 4,
                    file = out_pptx)

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

tax_table(physeq) %>% 
  data.frame() %>% 
  select(c("Best_Hit_ARO", "Strain")) %>% 
  group_by(Strain) %>% 
  add_count() %>% 
  arrange(-n)

## # A tibble: 131 × 3
## # Groups:   Strain [22]
##    Best_Hit_ARO          Strain               n
##    <chr>                 <chr>            <int>
##  1 emrR                  Escherichia coli    53
##  2 emrA                  Escherichia coli    53
##  3 emrB                  Escherichia coli    53
##  4 CTX-M-1               Escherichia coli    53
##  5 baeS                  Escherichia coli    53
##  6 CRP                   Escherichia coli    53
##  7 YojI                  Escherichia coli    53
##  8 evgS                  Escherichia coli    53
##  9 evgA                  Escherichia coli    53
## 10 Escherichia coli mdfA Escherichia coli    53
## # … with 121 more rows

Define colors for Taxonomic information:

ps_all %>%  speedyseq::psmelt() %>%  pull(Strain) %>%  unique() -> strains
set.seed(456)
col_strains <- distinctColorPalette(length(strains),
                                    runTsne = FALSE)

names(col_strains) <- strains

physeq %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #         by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #         suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Strain)) +  
  geom_bar( stat = "identity", colour="black", size=0.025) +
  facet_grid(Drug_Class_multi  ~  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("log10 TPM") + 
  theme_light() +
  scale_fill_manual(name = "", values = col_strains) +
  theme(legend.position = "bottom") -> p_mec

# p_mec %>% 
#   ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2779 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 12,
                    file = out_pptx)

## Warning: Removed 2779 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

physeq %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #         by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #         suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Drug_Class_multi)) +  
  geom_bar( stat = "identity", colour="black", size=0.025) +
  facet_grid( Strain   ~ Model +  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("log10 TPM") + 
  theme_light() +
  scale_fill_manual(name = "", values = col) +
  theme(legend.position = "bottom") -> p_mec

# p_mec %>% 
#   ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2779 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 12,
                    file = out_pptx)

## Warning: Removed 2779 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

# see Sneha's & Hannah's code for "plot.df.sep <- separate_rows(plot.df, Drug.Class,sep = '; ',convert = TRUE)"

physeq %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #         by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #         suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Strain)) +  
  geom_bar( stat = "identity", colour="black", position = "fill", size=0.05) +
  facet_grid(Drug_Class_multi  ~ Model +  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("Proportion") + 
  theme_light() +
  scale_fill_manual(name = "", values = col_strains) +
  theme(legend.position = "bottom") -> p_mec

# p_mec %>% 
#   ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2779 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 12,
                    file = out_pptx)

## Warning: Removed 2779 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

# see Sneha's & Hannah's code for "plot.df.sep <- separate_rows(plot.df, Drug.Class,sep = '; ',convert = TRUE)"

physeq %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #         by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #         suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = Strain)) +  
  geom_bar( stat = "identity", colour="black", size=0.025) +
  facet_grid(.  ~ Model +  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("log10 TPM") + 
  theme_light() +
  scale_fill_manual(name = "", values = col_strains) +
  theme(legend.position = "bottom") -> p_mec

# p_mec %>% 
#   ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2779 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 6,
                    file = out_pptx)

## Warning: Removed 2779 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

# see Sneha's & Hannah's code for "plot.df.sep <- separate_rows(plot.df, Drug.Class,sep = '; ',convert = TRUE)"

physeq %>%
  microbiome::transform(transform = "log10p") %>% 
  speedyseq::psmelt() %>% 
  # left_join(tax_mapping,
  #         by = c("Best_Hit_ARO" = "Best_Hit_ARO"),
  #         suffix = c("_x", "")) %>%
  mutate(Abundance = na_if(Abundance, 0)) %>%
  mutate(logTPM = log10(Abundance + 1))  %>% 
  # separate_rows(., Resistance_Mechanism,sep = '; ',convert = TRUE) %>% 
  # ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = fct_reorder(Strain, logTPM))) +   
  ggplot(aes(x = as.factor(Day_of_Treatment), y = Abundance, fill = reorder(Strain, logTPM, FUN=median, na.rm = TRUE))) +
  geom_bar( stat = "identity", position="fill", colour="black", size=0.025) +
  facet_grid(.  ~ Model +  Treatment + Fermentation + Antibiotic_mg.mL , scales = "free", space = "free_x") +
  ggtitle("ARG Type Abundance per Sample") +
  xlab("Day (Treatment)")  +
  ylab("Proportion") + 
  theme_light() +
  scale_fill_manual(name = "", values = col_strains) +
  theme(legend.position = "bottom") -> p_mec

# p_mec %>% 
#   ggpubr::set_palette("jco") -> p_mec

p_mec

## Warning: Removed 2779 rows containing missing values (position_stack).

p_mec %>%
  export::graph2ppt(append = TRUE, width = 14, height = 6,
                    file = out_pptx)

## Warning: Removed 2779 rows containing missing values (position_stack).

## Exported graph as ~/Projects/ETH/Alessia/mobilome/human/graph.pptx

sessionInfo()

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] GUniFrac_1.4         ape_5.6              microbiome_1.14.0   
##  [4] metagMisc_0.0.4      gdtools_0.2.3        reshape2_1.4.4      
##  [7] scales_1.1.1         randomcoloR_1.1.0.1  vegan_2.5-7         
## [10] lattice_0.20-45      permute_0.9-5        RColorBrewer_1.1-2  
## [13] microViz_0.9.0       here_1.0.1           ggrepel_0.9.1       
## [16] speedyseq_0.5.3.9018 phyloseq_1.36.0      forcats_0.5.1       
## [19] stringr_1.4.0        dplyr_1.0.7          purrr_0.3.4         
## [22] readr_2.1.0          tidyr_1.1.4          tibble_3.1.6        
## [25] ggplot2_3.3.5        tidyverse_1.3.1.9000
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           uuid_1.0-3             backports_1.4.1       
##   [4] systemfonts_1.0.2      plyr_1.8.6             igraph_1.2.10         
##   [7] splines_4.1.2          crosstalk_1.2.0        GenomeInfoDb_1.28.4   
##  [10] digest_0.6.29          foreach_1.5.1          htmltools_0.5.2       
##  [13] lmerTest_3.1-3         fansi_0.5.0            magrittr_2.0.1        
##  [16] cluster_2.1.2          tzdb_0.2.0             openxlsx_4.2.4        
##  [19] Biostrings_2.60.2      modelr_0.1.8           matrixStats_0.61.0    
##  [22] stabledist_0.7-1       officer_0.4.0          colorspace_2.0-2      
##  [25] rvest_1.0.2            haven_2.4.3            xfun_0.28             
##  [28] crayon_1.4.2           RCurl_1.98-1.5         jsonlite_1.7.2        
##  [31] lme4_1.1-27.1          survival_3.2-13        iterators_1.0.13      
##  [34] glue_1.6.0             rvg_0.2.5              gtable_0.3.0          
##  [37] zlibbioc_1.38.0        XVector_0.32.0         V8_3.6.0              
##  [40] car_3.0-11             Rhdf5lib_1.14.2        BiocGenerics_0.38.0   
##  [43] abind_1.4-5            pheatmap_1.0.12        DBI_1.1.1             
##  [46] rstatix_0.7.0          Rcpp_1.0.7             viridisLite_0.4.0     
##  [49] xtable_1.8-4           clue_0.3-60            foreign_0.8-81        
##  [52] DT_0.20                stats4_4.1.2           timeSeries_3062.100   
##  [55] htmlwidgets_1.5.4      httr_1.4.2             ellipsis_0.3.2        
##  [58] spatial_7.3-14         pkgconfig_2.0.3        farver_2.1.0          
##  [61] sass_0.4.0             dbplyr_2.1.1           utf8_1.2.2            
##  [64] tidyselect_1.1.1       labeling_0.4.2         rlang_0.4.12          
##  [67] munsell_0.5.0          cellranger_1.1.0       tools_4.1.2           
##  [70] cli_3.1.0              generics_0.1.1         devEMF_4.0-2          
##  [73] ade4_1.7-18            export_0.3.0           broom_0.7.11          
##  [76] evaluate_0.14          biomformat_1.20.0      fastmap_1.1.0         
##  [79] yaml_2.2.1             knitr_1.36             fs_1.5.2              
##  [82] zip_2.2.0              rgl_0.107.14           nlme_3.1-153          
##  [85] xml2_1.3.2             compiler_4.1.2         rstudioapi_0.13       
##  [88] curl_4.3.2             ggsignif_0.6.3         reprex_2.0.1          
##  [91] statmod_1.4.36         statip_0.2.3           bslib_0.3.1           
##  [94] stringi_1.7.6          highr_0.9              modeest_2.4.0         
##  [97] stargazer_5.2.2        fBasics_3042.89.1      Matrix_1.3-4          
## [100] nloptr_1.2.2.2         ggsci_2.9              multtest_2.48.0       
## [103] vctrs_0.3.8            pillar_1.6.4           lifecycle_1.0.1       
## [106] rhdf5filters_1.4.0     jquerylib_0.1.4        data.table_1.14.2     
## [109] bitops_1.0-7           flextable_0.6.9        stable_1.1.4          
## [112] R6_2.5.1               rio_0.5.27             IRanges_2.26.0        
## [115] codetools_0.2-18       boot_1.3-28            MASS_7.3-54           
## [118] assertthat_0.2.1       rhdf5_2.36.0           rprojroot_2.0.2       
## [121] withr_2.4.3            S4Vectors_0.30.2       GenomeInfoDbData_1.2.6
## [124] mgcv_1.8-38            parallel_4.1.2         hms_1.1.1             
## [127] rpart_4.1-15           timeDate_3043.102      grid_4.1.2            
## [130] minqa_1.2.4            rmarkdown_2.11         rmutil_1.1.5          
## [133] carData_3.0-4          Rtsne_0.15             ggpubr_0.4.0          
## [136] numDeriv_2016.8-1.1    Biobase_2.52.0         lubridate_1.8.0       
## [139] base64enc_0.1-3

Resistome_visualization

Florentin Constancias

January 07, 2022

Load required packages

Source required functions

Import data and define variables: